data-scientist

name

data-scientist

description

Execute data science workflows from exploration to production. Apply machine learning modeling, statistical analysis, A/B testing, causal inference, feature engineering, model evaluation, and MLOps patterns. Triggers on "build predictive model", "design A/B test", "feature engineering", "model evaluation", "causal inference", "productionize ML", "choose ML algorithm", "statistical analysis", "model monitoring", or "data science workflow". Distributed RL training systems: ml-systems-engineer-rl-engineering.

Data Scientist

Overview

Execute data science workflows from exploration to production. This skill covers machine learning modeling, statistical analysis, A/B testing, causal inference, feature engineering, model evaluation, and MLOps patterns.

Features

• ML modeling lifecycle: problem framing, data prep, model selection, training, evaluation
• Statistical analysis: hypothesis testing, regression, ANOVA, Bayesian methods
• A/B testing: experiment design, sample size calculation, statistical power, result interpretation
• Causal inference: propensity score matching, difference-in-differences, instrumental variables
• Feature engineering: encoding, scaling, selection, dimensionality reduction
• MLOps: model deployment, monitoring, drift detection, retraining triggers

Usage

1. Identify the user's data science need (modeling, analysis, experimentation, or MLOps)
2. Follow the corresponding workflow below
3. Produce structured outputs: model cards, experiment reports, feature engineering pipelines, or MLOps runbooks

Examples

• User: "Build a churn prediction model" Agent: Runs ML Modeling workflow, frames problem, selects features, trains classifier, evaluates with precision/recall, produces model card

• User: "Design an A/B test" Agent: Runs Experiment Design workflow, calculates sample size, defines success metrics, sets up randomization, produces experiment plan

• User: "Monitor model drift" Agent: Runs MLOps workflow, defines drift metrics, sets up monitoring dashboard, configures retraining triggers

When to Use

• Scoping ML problems, baselines, feature engineering, and model evaluation
• Designing A/B tests, power analysis, or causal inference when experiments are infeasible
• Productionizing models (batch, real-time, monitoring, retraining triggers)
• Selecting ML, stats, or MLOps tools for a given problem and data regime

When NOT to Use

• Executive dashboards, KPI definitions, or BI storytelling → use bi-analyst
• Warehouse dimensional modeling or ETL idempotency patterns → use data-warehouse-engineer
• Prompt design, LLM agents, or guardrailed GenAI features → use prompt-engineer
• RL training platform, rollout workers, distributed PPO/SAC jobs → use ml-systems-engineer-rl-engineering
• Revenue metrics (ARR, NRR) or ASC 606 accounting → use senior-revenue-accountant

Core Workflows

1. End-to-End ML Project Workflow

Phase checklist:

1. Problem definition

   • Define the business metric to optimize
   • Determine if ML is needed (rule-based may suffice)
   • Set success criteria and failure modes

2. Data exploration & validation

   • Profile distributions, missing values, duplicates
   • Check for leakage (future information in training data)
   • Validate data freshness and coverage

3. Feature engineering

   • Create domain-relevant features
   • Encode categoricals, scale numerics
   • Document feature definitions and dependencies

4. Modeling

   • Baseline: simple model first (linear regression, logistic)
   • Iterate: tree-based, then ensembles, then deep learning if needed
   • Cross-validate properly (time-based for temporal data)

5. Evaluation

   • Hold-out test set, never used for hyperparameter tuning
   • Check calibration, fairness, robustness
   • Compare against baseline and business threshold

6. Production

   • Serialize model, build inference API
   • Add monitoring (prediction drift, latency)
   • Document retraining triggers

2. Statistical Analysis & Experimentation

A/B testing workflow:

1. Define hypothesis, primary metric, and minimal detectable effect (MDE)
2. Calculate sample size (power analysis)
3. Randomize and run experiment
4. Check invariant metrics (randomization sanity)
5. Analyze primary metric with proper statistical test
6. Correct for multiple comparisons if needed
7. Document and socialize results

Causal inference when A/B test is impossible:

• Difference-in-differences
• Propensity score matching
• Instrumental variables
• Regression discontinuity

3. Productionizing Models (MLOps)

Deployment patterns:

Pattern	When	Trade-off
Batch scoring	Periodic predictions, no latency requirement	Simple, stale predictions between runs
Real-time API	User-facing, latency-sensitive	Complex, requires monitoring
Edge / on-device	Mobile/IoT, offline needed	Model size constraints, hard to update
Embedded	Database/warehouse native (BigQuery ML, Snowpark)	Limited to supported algorithms

Monitoring checklist:

• Prediction distribution drift vs training
• Feature drift (incoming data changes)
• Latency and throughput
• Error rate and fallback behavior
• Business metric tracking

4. Tool Selection

Task	Recommended Tools
Data manipulation	pandas, Polars, SQL
Feature engineering	scikit-learn, Feature-engine, Tsfresh
Modeling	scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow
Experiment tracking	MLflow, Weights & Biases, Neptune
Hyperparameter tuning	Optuna, Ray Tune, Hyperopt
Causal inference	CausalML, DoWhy, EconML
Interpretability	SHAP, LIME, ELI5
Deployment	FastAPI, BentoML, Seldon, SageMaker